text: from pyimagesearch.transform import four_point_transform
  from skimage.filters import threshold_local
  import argparse
  import cv2
  import imutils
  
  ap = argparse.ArgumentParser()
  ap.add_argument("-i", "--image", required=True,
                  help="Path to the image to be scanned")
  args = vars(ap.parse_args())
  
  image = cv2.imread(args["image"])
  ratio = image.shape[0] / 500.0
  orig = image.copy()
  image = imutils.resize(image, height=500)
  
  gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  gray = cv2.GaussianBlur(gray, (5, 5), 0)
  edged = cv2.Canny(gray, 75, 200)
  
  print("STEP 1: EDGE DETECTION")
  cv2.imshow("Image", image)
  cv2.imshow("Edged", edged)
  cv2.waitKey(0)
  cv2.destroyAllWindows()
  
  cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
  cnts = cnts[0] if imutils.is_cv2() else cnts[1]
  cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
  for c in cnts:
      peri = cv2.arcLength(c, True)
      approx = cv2.approxPolyDP(c, 0.02 * peri, True)
  
      if len(approx) == 4:
          screenCnt = approx
          break
  
  print("STEP 2: FIND CONTOURS OF PAPER")
  cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
  cv2.imshow("Outline", image)
  cv2.waitKey(0)
  cv2.destroyAllWindows()
  
  warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
  
  warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
  t = threshold_local(warped, 11, offset=10, method="gaussian")
  warped = (warped > t).astype("uint8") * 255
  
  print("STEP 3: Apply perspective transform")
  cv2.imshow("Original", imutils.resize(orig, height = 650))
  cv2.imshow("Scanned", imutils.resize(warped, height = 650))
  cv2.waitKey(0)
  